Scalable Photonic Digital-to-Analog Converters
Bibliographic record
Abstract
This work introduces a novel architecture for implementing a parallel coherent photonic digital-to-analog converter (PDAC), designed to transform parallel digital electrical signals into corresponding analog optical output, convertible to analog electrical signals using photodiodes. The proposed architecture incorporates microring resonator-based modulators (MRMs), phase shifters, and symmetric multimode interference couplers. Efficient modulation is achieved by MRMs utilizing carrier depletion-induced refractive index changes, while metal heaters facilitate tuning of the ring resonator resonance wavelength. The proposed architecture is scalable to higher bit resolutions and exhibits a dynamic range limited by MRM’s sensitivity to applied bias and noise levels. Experimental results of the fabricated chip in the silicon-on-insulator (SOI) platform showcase the successful realization of a 4 GSample/sec conversion rate in a 2-bit resolution operation, along with a stationary conversion of four parallel DC digital signals into 16 analog intensity levels in a 4-bit PDAC configuration. The study encompasses a proof-of-concept experimental demonstration of 8 Gbps data conversion, along with a 50 Gbps data conversion rate using the optimized design in the simulation, affirming the accuracy and quality of the PDAC architecture. These findings contribute to the advancement of PDAC technology, providing insights into performance characteristics, limitations, and potential applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".